SST-GCN: The Sequential based Spatio-Temporal Graph Convolutional networks for Minute-level and Road-level Traffic Accident Risk Prediction

TL;DR

This paper introduces SST-GCN, a novel model combining graph convolutional networks and LSTM to predict minute-level and road-level traffic accident risks, effectively capturing spatial and temporal road dynamics.

Contribution

The paper presents SST-GCN, a new spatio-temporal graph neural network that improves traffic accident risk prediction at fine-grained temporal and spatial scales.

Findings

SST-GCN outperforms existing models in minute-level accident risk prediction.

The model effectively captures complex spatial-temporal road relationships.

Experiments conducted on Seoul data validate the model's superior performance.

Abstract

Traffic accidents are recognized as a major social issue worldwide, causing numerous injuries and significant costs annually. Consequently, methods for predicting and preventing traffic accidents have been researched for many years. With advancements in the field of artificial intelligence, various studies have applied Machine Learning and Deep Learning techniques to traffic accident prediction. Modern traffic conditions change rapidly by the minute, and these changes vary significantly across different roads. In other words, the risk of traffic accidents changes minute by minute in various patterns for each road. Therefore, it is desirable to predict traffic accident risk at the Minute-Level and Road-Level. However, because roads have close and complex relationships with adjacent roads, research on predicting traffic accidents at the Minute-Level and Road-Level is challenging. Thus, it is essential to build a model that can reflect the spatial and temporal characteristics of roads for traffic accident prediction. Consequently, recent attempts have been made to use Graph Convolutional Networks to capture the spatial characteristics of roads and Recurrent Neural Networks to capture their temporal characteristics for predicting traffic accident risk. This paper proposes the Sequential based Spatio-Temporal Graph Convolutional Networks (SST-GCN), which combines GCN and LSTM, to predict traffic accidents at the Minute-Level and Road-Level using a road dataset constructed in Seoul, the capital of South Korea. Experiments have demonstrated that SST-GCN outperforms other state-of-the-art models in Minute-Level predictions.